Vision-Based Reactive Planning for Aggressive Target Tracking While Avoiding Collisions and Occlusions
Bryan Penin, Paolo Robuffo Giordano, François Chaumette · IEEE Robotics and Automation Letters · 2018
In this letter, we investigate the online generation of optimal trajectories for target tracking with a quadrotor while satisfying a set of image-based and actuation constraints. We consider a quadrotor equipped with a camera (either down or front-looking) with limited field of view. The aim is to follow in a smooth but reactive way a moving target while avoiding obstacles in the environment and occlusions in the image space. We propose vision-based approaches based on multiobjective optimization, especially with the occlusion constraint formulation. We design an online replanning strategy inspired from model predictive control that successively solves a nonlinear optimization problem. The problem is formulated as a nonlinear program (NLP) using differential flatness and finite parametrization with B-Splines. This allows a resolution by sequential quadratic programming (SQP) at a rate of 30 Hz. The robustness and reactivity of the replanning algorithm are demonstrated through realistic simulation results. Experiments validating the performance with a real quadrotor are also presented.